Sprint Cycle Time Calculator

Calculator Inputs

Use sprint dates or manual working days. Dates override the manual sprint length when both are provided.

Sprint start date

Sprint end date

Manual sprint working days

Team size

Committed sprint items

Completed sprint items

Average WIP

WIP limit

Average active work days

Average review days

Average testing days

Average blocked days

Average queue days

Reopen or rework rate (%)

Variability factor (%)

Target cycle time (days)

Working hours per day

Example Data Table

This sample shows item-level timings you could average before entering summary values into the calculator.

Work Item	Started	Completed	Active	Review	Testing	Blocked	Queue	Cycle Time
ST-101	2026-03-02	2026-03-07	2.0	0.7	0.8	0.4	0.6	4.5
ST-102	2026-03-02	2026-03-08	3.1	0.9	1.0	0.5	0.7	6.2
ST-103	2026-03-03	2026-03-09	2.9	1.0	1.2	0.8	0.6	6.5
ST-104	2026-03-04	2026-03-09	2.4	0.8	1.0	0.3	0.5	5.0
ST-105	2026-03-04	2026-03-10	3.0	1.1	1.2	0.9	0.8	7.0

Formula Used

Base Touch Time = Active Days + Review Days + Testing Days Rework Days = Reopen Rate × Base Touch Time Cycle Time = Base Touch Time + Blocked Days + Queue Days + Rework Days Flow Efficiency = (Base Touch Time + Rework Days) ÷ Cycle Time × 100 Throughput per Day = Completed Items ÷ Sprint Working Days Little’s Law Cycle Estimate = Average WIP ÷ Throughput per Day Delivery Rate = Completed Items ÷ Committed Items × 100 P85 Forecast = Cycle Time × (1 + Variability Factor)

This calculator blends stage-based timing with Little’s Law. Stage timing explains where time goes. Little’s Law checks whether WIP and throughput support the same story. Large gaps can indicate inconsistent intake, batching, or unstable completion rates.

How to Use This Calculator

Enter sprint dates or type manual working days.
Add committed and completed item counts for the sprint.
Enter average WIP and your current WIP limit.
Fill stage averages for active work, review, testing, blocked time, and queue time.
Enter rework rate, target cycle time, and expected variability.
Click the calculate button to show results above the form.
Review the chart, summary metrics, and health label.
Export the results as CSV or PDF when needed.

FAQs

1. What is sprint cycle time?

Sprint cycle time is the average elapsed time from starting work on an item to completing it. It includes active effort, waiting, blocked time, testing, review, and rework within the sprint flow.

2. How is cycle time different from lead time?

Cycle time starts when the team begins active work. Lead time starts when the request is created or accepted. Lead time is broader because it includes any time before development actually begins.

3. Why does the calculator use Little’s Law too?

Little’s Law gives a useful cross-check: cycle time should roughly match average WIP divided by throughput. If the two numbers differ a lot, your averages may hide batching, unstable WIP, or inconsistent completion behavior.

4. What does flow efficiency tell me?

Flow efficiency shows how much of total cycle time is active touch time instead of waiting. A higher percentage usually means smoother flow, fewer handoff delays, and better delivery predictability.

5. Should blocked and queue days be tracked separately?

Yes. Blocked time usually points to dependencies, approvals, or missing information. Queue time often reflects handoffs, batching, or overloaded stages. Keeping them separate makes bottlenecks easier to fix.

6. What is a good target cycle time?

A good target depends on work size, team maturity, service expectations, and product risk. Start with your recent median or average, then reduce it gradually while watching quality, rework, and delivery consistency.

7. Why include a rework rate?

Rework adds real cycle time even when tickets appear finished briefly. Including it helps you plan more honestly, especially when review findings, test failures, or scope corrections frequently send work backward.

8. When should I use the P85 forecast?

Use the P85 forecast when you need a safer planning number for commitments, stakeholder updates, or dependency coordination. It adds a variability buffer, so estimates are less optimistic than plain averages.